Consumers may find their ideal retirement community using current methods such as paid advertising, utilizing the American Association of Retired Persons (AARP), profiling a market with geographic proximity data, and/or profiling demographic data such as home value, and/or taxes. However, a consumer would not know about specific retirement communities in the market, and a discretionary spending behavior profile of those communities which might be important to a prospective retiree. This data may help estimate true expenses living in the community and determine whether the activities the consumer likes to spend on would be available in their retirement community. Additionally, this data may benefit many merchants, advertisers, and other entities who are often interested in determining what kind of information influences consumers, analyzing such information, and determining how they can use the information to their advantage.
However, while many methods and systems have been developed to measure the effectiveness of various types of influences, such as advertising, coupons or offers, consumer reviews, and the like, many methods and systems fail to identify, let alone measure, the effectiveness providing an accurate retirement community for consumers. Further, traditional approaches to determining an appropriate retirement community are labor intensive, often inaccurate or incomplete, lack objectivity, and are often based on impressions rather than facts.
Thus, there is a need for a technical solution to find an appropriate and even ideal retirement community for prospective retirees.